Online Optimization of Industrial FCC Unit Based on PSO Algorithm and RBF Neural Network

摘要：

The Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA) are two of the most powerful methods to solve the unconstrained and constrained global optimization problems. In this paper, these two methods are briefly introduced firstly, and then the online rolling optimization of industrial FCC unit is carried out based on the RBF Neural Network predictive model. The results of simulation based on the two optimization methods are compared. The comparative results show that the PSO can perform well as the GA in searching the global optimal position. Furthermore, the PSO runs much faster which makes it more effective in online optimization.

关键词： PSO GA RBF Neural Network Online Optimization FCC

作者: Yi Deng Qingyin Jiang Zhikai Cao

作者单位: Department af Chemical & Biochemical Engineering, College of Chemistry & Chemical Engineering Xiamen Department of Chemical & Biochemical Engineering, College of Chemistry & Chemical Engineering Xiamen

会议类型: 国际会议

会议名称: 2010 Second Asia-Pacific Conference on Information Processing(2010年第二届亚太地区信息处理国际会议 APCIP 2010)

会议地点: 南昌

会议语种:英文

页码: 353-356

在线出版日期: 2010-09-17（万方平台首次上网日期，不代表论文的发表时间）